Hi, I have fit a series of ols() models, by group, in this manner:
l <- ols(y ~ rcs(x, 4)) ... where the series of 'x' values in each group is the same, however knots are not always identical between groups. The result is a table of 'coefs' derived from the ols objects, by group: group Intercept top top' top'' 1 6.864 0.01 2.241 -2.65 2 6.836 0.047 -0.556 0.606 3 5.877 -0.019 0.084 -0.175 4 6.021 -0.003 0.121 -0.128 5 7.164 0.014 0.031 -0.096 I would like to describe groups of relationships, based on the coefficients, however I am not sure if they are directly comparable. In addition, I would like to regress these coefs on another set of variables, with the aim of predicting a series of RCS coefficients along external gradients. In essence, I am hoping to use RCS coefficients to summarize y ~ rcs(x), in a way that can then me modeled like this: [y ~ rcs(x)] ~ z. Is this interpretation of RCS coefficients even possible? If not, would forcing knot locations make it a possibility? Or, would modeling both knots and RCS coefs with external variables lead to sensible predictions? Cheers, Dylan -- Dylan Beaudette Soil Resource Laboratory http://casoilresource.lawr.ucdavis.edu/ University of California at Davis 530.754.7341 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
